Nowadays merchants, service providers, financial institutions and the like use consumer account data to tailor advertisements, make credit determinations, and determine other relevant information. However, consumer transactions and even payments are often volatile and sparse. Further, the products and services provided by the merchant, service provider, or payment provider may vary widely. For example, a payment provider like PayPal who may have upwards of 200 million active accounts, may have a part of those active accounts belong to causal consumers that make few and infrequent payments. As another example, a consumer with an account at a financial institution may complete transactions using the account at a bakery in the morning and a purchase for tires in the afternoon. These particular traits, where sparse data exists, create significant hurdles in statistical learning-based solutions such as transaction risk models (classification) and abnormality detection models (clustering). This is because the traditional statistical learning algorithms (e.g., linear regression) are less effective in summarizing cases that are abundant in sparse transaction data. Further, current statistical models cannot handle the big data and thus do not converge in a timely manner. Therefore it would be beneficial to create a model that enables the classification of large data using deep machine learning-based techniques.
Embodiments of the present disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, whereas showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same.